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Impact of momentum bias on forecasting through knowledge discovery techniques in the foreign exchange market

Authors
Chun, SHKim, SH
Issue Date
Jan-2003
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
forecasting; foreign exchange; knowledge discovery; neural network; case based reasoning
Citation
EXPERT SYSTEMS WITH APPLICATIONS, v.24, no.1, pp.115 - 122
Journal Title
EXPERT SYSTEMS WITH APPLICATIONS
Volume
24
Number
1
Start Page
115
End Page
122
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/16224
DOI
10.1016/S0957-4174(02)00089-1
ISSN
0957-4174
Abstract
To an increasing extent since the late 1980s, software learning methods including neural networks (NN) and case based reasoning (CBR) have been used for prediction in financial markets and other areas. In the past, the prediction of foreign exchange rates has focused on isolated techniques, as exemplified by the use of time series models including regression models or smoothing methods to identify cycles and trends. At best, however, the use of isolated methods can only represent fragmented models of the causative agents, which underlie business cycles. Experience with artificial intelligence applications since the early 1980s points toward a multistrategy approach to discovery and prediction. This paper investigates the impact of momentum bias on forecasting financial markets through knowledge discovery techniques. Different modes of bias are used as input into learning systems using implicit knowledge representation (NNs) and CBR. The concepts are examined in the context of predicting movements in the Japanese yen. (C) 2002 Elsevier Science Ltd. All rights reserved.
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